tbuckley/cabot-search huggingface dataset:
AI-driven real-time patient identification for randomized controlled trials.
Authors: Gavin Miyasato, Vamsi Chandra Kasivajjala, Mohit Misra, Kiran Kumar, Amrut Sadashiv Kadam, Howard S Friedman
Journal: Journal of Clinical Oncology (2023)

e13565 Background: In India, clinical trial recruitment is a challenging process, often relying on the time- and labor-intensive process of investigators reviewing handwritten charts to identify potential candidates. We developed an AI-driven, real-time solution to address this issue and demonstrated its feasibility by identifying patients that would have been candidates for a 2017-2018 study of metastatic breast cancer (mBC) patients who failed prior chemotherapy. The original trial recruited 46 patients across 16 sites over 7 months in India. Methods: The study protocol was reviewed and applied to six years (2017-2022) of retrospective electronic medical records (EMR) data from a cancer hospital in India’s National Capital Region. Trial inclusion and exclusion criteria were translated to data elements which were extracted from both structured (e.g., lab) and unstructured (e.g., clinical notes) EMR data. For the unstructured data specifically, natural language processing algorithms were developed and implemented over the span of three weeks to extract the specific data elements. The veracity of the extracted data was confirmed via manual review by an oncologist. To assess the sensitivity of the results to the study criteria and provide a range of the potential candidate population size, the criteria were applied in two ways – as a strict application (e.g., ECOG must be reported and must be &lt; = 2) and as a relaxed application (e.g., ECOG may be unreported or, if reported, must be &lt; = 2). Results: Among the initial cohort of 112,239 females between 18 and 65 years of age, 3,203 (3%) were identified as having mBC and failure of prior chemotherapy or relapse. 537 patients remained after applying all inclusion criteria (ECOG status &lt; = 2, LVEF &gt; = 50%, no prior radiotherapy, prior chemo completed 4+ weeks prior, adequate bone marrow, renal, and hepatic function). Exclusion criteria (prior taxane exposure, 2+ prior chemotherapies, cardiac conditions, CNS lesions, motor/sensory neurotoxicities, HBV/HCV/HIV infection) were then applied, resulting in 37 patients that would be potential candidates for the trial, per the strict application of criteria. The relaxed version of the analysis yielded a candidate pool that was five times larger than the strict application, which was largely driven by cases where information was unavailable in the EMR. Conclusions: We demonstrated that the use of an AI-driven platform to apply clinical trial criteria to Indian real-world data can provide an efficient solution to identifying candidate trial patients. Once the criteria are defined and algorithms are developed, patient identification can happen in real time and reduce patient recruitment timelines by months. Future applications will include other cancer types and expand the breadth of data elements extracted to enable the rapid assessment of additional inclusion and exclusion criteria.
Prognostic impact of adjuvant endocrine therapy for estrogen receptor-positive and HER2-negative T1a/bN0M0 breast cancer.
Authors: Yuko Takahashi, Shinsuke Sasada, Naoto Kondo, Hiroya Hashimoto, Kaori Terata, Yasuaki Sagara, Yoichi Naito, Kumiko Kida, Takayuki Ueno, Keisei Anan, Akihiko Suto, Chizuko Kanbayashi, Mina Takahashi, Rikiya Nakamura, Toshiyuki Ishiba, Michiko Tsuneizumi, Seiichiro Nishimura, Fumikata Hara, Tadahiko Shien, Hiroji Iwata
Journal: Journal of Clinical Oncology (2023)

538 Background: Adjuvant endocrine therapy (ET) is the standard of care for Estrogen Receptor (ER)-positive early-stage breast cancer. However, many patients suffer from adverse events associated with ET. The prognosis of patients with T1a/bN0 breast cancer with ER-positive and human epidermal growth factor 2 (HER2)-negative is excellent, and it is not clear whether adjuvant ET should be recommended. Here we evaluate the effect of adjuvant ET for ER-positive and HER2-negative T1a/bN0M0 breast cancer. Methods: This study was a multicenter, retrospective cohort study that evaluated adjuvant ET for patients with ER-positive and HER2-negative T1a/bN0M0 breast cancer. Patients who underwent surgery between 2008 and 2012 in 47 institutions of the Japan Clinical Oncology Group (JCOG) breast cancer group were eligible. We analyzed the cumulative incidence of distant metastasis as primary endpoint using the Gray test model and time to event endpoints (distant disease free survival and overall survival) using log-rank test between patients treated with and without adjuvant ET. We also used Gray test model for the analysis of the cumulative incidence of ipsilateral and contralateral breast cancer. Predictive factors were assessed using the Fine-Gray model and the Cox proportional hazards model. Results: The median follow-up period was 9.2 years. Of 4758 eligible patients (1202 T1a [25.3%] and 3556 T1b [74.7%] diseases), 3991 (83.9%) received adjuvant ET. The 9-year cumulative incidence of distant metastasis was 1.5% with ET and 2.6% without ET (adjusted subdistribution hazard ratio [sHR], 0.54; 95% CI, 0.32-0.93). In the multivariate analysis, the independent risk factors of distant metastasis were no ET, mastectomy, high grade, and lymphatic invasion. The 9-year overall survival was 97.0% and 94.4% (HR, 0.54; 95% CI, 0.38-0.77). Adjuvant ET reduced the incidence of ipsilateral and contralateral breast cancer (9-year rates; 1.1% vs 6.9%; sHR, 0.17; 95% CI 0.11-0.28, and 1.9% vs 5.2%; sHR, 0.33; 95% CI, 0.22-0.49). Conclusions: Adjuvant endocrine therapy significantly reduces distant metastases, however the absolute difference is small for ER-positive and HER2-negative T1a/bN0M0 breast cancer. Furthermore, it reduced ipsilateral and contralateral breast cancer as well. Shared decision making regarding the omission of adjuvant endocrine therapy is crucial, especially in patients with low risk of distant metastasis.
An intelligent machine learning model for real-time early detection of undesirable cancer events: AIM2REDUCE.
Authors: Robert C. Grant, Muammar Kabir, Baijiang Yuan, Benjamin Grant, Sharon Narine, Kevin He, Rami Ajaj, Luna Jia Zhan, Aly Fawzy, Janine Xu, Yuhua Zhang, Vivien Yu, Conor French, Wei Xu, Rahul G. Krishnan, Steven Gallinger, Monika K. Krzyzanowska, Tran Truong, Geoffrey Liu
Journal: Journal of Clinical Oncology (2023)

1557 Background: Cancer and its treatment cause undesirable cancer events (UCEs). Automated warning systems could reduce the frequency and severity of UCEs by alerting the healthcare team and allocating preventative interventions. Most previous studies predicted single UCEs at the initiation of treatment. In AIM2REDUCE, we developed and evaluated a general-purpose system for predicting UCEs during outpatient systemic anti-cancer therapy. Methods: Each time a patient receives treatment, AIM2REDUCE applies machine learning to the preceding data in the electronic medical record (EMR) to predict future UCEs. We identified patients treated for aerodigestive cancers from the EMR at Princess Margaret Cancer Centre, who were randomly split into development, validation, and test cohorts. Features included cancer diagnosis, treatment sessions with doses, laboratory tests, and patient-reported symptoms. UCEs are listed in the Table. We trained LASSO regression and random forests models in the training cohort and tuned hyperparameters in the validation cohort using Bayesian optimization. We evaluated performance across discrimination, calibration, and net benefit in the test cohort. Results: The cohort included 5,760 patients who received 175,565 treatment sessions, with 13,612,746 unique data points across 102 features. Of these patients, 2,352 (40.8%) were female, the median age was 64.0 years (interquartile range 14.0), the most common diagnoses were lung cancer (2,071, 36.0%) and pancreatic cancer (926, 16.1%), and the most common treatment regimens were weekly gemcitabine (433, 7.5%) and maintenance pemetrexed (417, 7.2%). The Table shows the performance of AIM2REDUCE. Conclusions: We demonstrate that longitudinal machine learning systems trained using EMR data can accurately predict a wide range of UCEs. Based on these results, automated warning systems should be implemented and evaluated in real-time clinical practice. [Table: see text]
Genomic and transcriptomic profiling of inflammatory breast cancers and non-inflammatory breast cancers in Asian women.
Authors: Kaiwen Zhou, Ying Lin, Mengmeng Zhang, Nan Shao, Zhen Shan
Journal: Journal of Clinical Oncology (2023)

e12546 Background: Inflammatory breast cancer (IBC) is the most aggressive form of breast cancer. Despite its rare incidence, it accounts for 10% of breast cancer-caused mortality. Previous studies based on NGS have focused on western populations and found no common genomic and transcriptomic difference that could distinguish IBC from non-IBC. Methods: We performed WES on 15 IBC samples and their paired normal blood to identify somatic genomic alterations, copy number variants and large structural variants. RNA-seq was performed on 16 IBC samples to examine the RNA expression, pathway enrichment and gene fusions. WES and RNA-seq data were further investigated to discover genes that were dysregulated in both genomics and transcriptomics. For comparison, we performed WES and RNA-seq on 5 non-IBC samples and acquired additional 33 non-IBC samples of Asian women from the Cancer Genome Atlas Program (TCGA). Results: Apart from results similar to the previous reports in western IBC, ZNF74 and DYNC2H1 were identified as Asian-IBC-specific somatic mutations in our cohort. Elevated HRD mutational signature and scarHRD score indicated increased HRD events in Asian IBC patients. Structural variation analysis showed that deletions were found more frequent in IBC. RNA-seq indicated increased immune activation in IBC samples, but the immune cell infiltration within the tumor microenvironment were similar between IBC and non-IBC. Multiple gene fusions were more frequently found in IBC, including CTC-786C10.1—RP11-680G10.1, TULP4—RP11-732M18.3, RN7SL4P—RN7SL173P. Combined analysis of WES and RNA-seq showed more frequent mutation and increased pathway enrichment of RAS pathway in IBC, which may explain the aggressive biological behavior of IBC, inhibitors such as sorafenib, tipifarnib and trametinib targeting RAS signaling and its downstream pathways were predicted to possess promising effects in IBC treatment. Conclusions: Our study is the first to provide a complete genomic and transcriptomic landscape of IBC based on combined analysis of WES and RNA-seq and the first to focus on Asian women. We discovered intriguing differences unique in Asian women that could potentially explain IBC etiology and presented RAS signaling pathway as a potential therapeutic target in IBC treatment.
Accuracy of a clinical decision support system in identifying patients at risk of cancer in primary care.
Authors: Bea Bakshi, Miles Payling
Journal: Journal of Clinical Oncology (2023)

1562 Background: 85% of patients diagnosed with cancer are symptomatic at presentation. Yet a delay in diagnosis (e.g. missing a presentation) can result in late stage detection and poorer outcomes. Clinical decision support systems (CDSS) have been used in other fields to calculate risk and support standardization of care. However, little has been reported about successful implementation of CDSS with determining cancer risk. C the Signs is a CDSS which uses artificial intelligence, mapped with the latest evidence and guidance, to assist healthcare professionals in identifying patients at risk of cancer and determining the most appropriate pathway for diagnosis. The aim of this study is to assess the accuracy of C the Signs in a real-world setting. Methods: A retrospective observational study was conducted in the National Health Service in England utilizing all patients risk assessed for cancer through the Electronic Healthcare Record (EHR) integrated C the Signs platform between 1st January 2021 and 31st August 2022. A total of 118,677 patients were risk assessed. All patients were followed up for 6 months post risk assessment to determine outcome. A sub-analysis was performed on the patients who were flagged at risk of cancer through C the Signs and subsequently diagnosed with cancer to establish the CDSS’s cancer origin accuracy (the ability for the system to accurately predict which cancer the patient is at risk of). Results: 7,295 patients were diagnosed with cancer within 6 months of a C the Signs risk assessment. 7,056 patients were successfully identified at risk of cancer by the CDSS with a sensitivity of 96.7%. There were 103,168 false positives with a specificity of 7.37%. Of the 8,453 patients who were identified as not at risk of cancer using the CDSS, 239 had a confirmed cancer diagnosis within 6 months of risk assessment (with a negative predictive value 97.2%). The cancer origin accuracy of C the Signs was 85.6% across all cancers. Conclusions: A cancer CDSS like C the Signs can be used to accurately identify patients at risk of cancer. More research is needed to understand the specificity and false positive rate, and the impact of identifying these patients with non-cancer disease.
In-hospital outcomes of patients with breast cancer and venous thromboembolism.
Authors: Ankushi Sanghvi, Dhairya Nanavaty, Kannu Bansal, Gayatri Narayan, Shailja Shah
Journal: Journal of Clinical Oncology (2023)

e13133 Background: Breast cancer is the most common cause of cancer in women in the USA after skin cancer. About 264000 cases of breast cancer are diagnosed in women each year in the USA. 42000 women die from this cancer each year. Less is known regarding the association of venous thromboembolism (VTE) in breast cancer. We aim to study the prevalence of VTE in breast cancer and its effect on healthcare utilization using a national database. Methods: We used the National Inpatient Sample (NIS) to collect data for all adult patients &gt;18 years with a diagnosis of breast cancer from 2017-2019 using appropriate ICD 10 codes. These patients were further classified into patients with and without DVT/PE. Multivariate regression analysis was performed to study the effect of DVT/PE in patients with breast cancer taking mortality as primary outcome and total cost and length of hospital stay as secondary outcomes. Results: There were a total of 855494 admissions with a diagnosis of breast cancer. The mean age was 63.87 years and 98.8% of them were females. Acute DVT was found in 1.87% of patients and acute PE was found in 3.01% of patients with breast cancer. A total of 7.36% of breast cancer patients who had acute DVT and 8.04% with acute PE died during the hospital stay. The odds of all-cause mortality for DVT (OR 1.58; 95% CI 1.38-1.82, p&lt;0.001) and PE (OR 1.71, 95% CI 1.52-1.94, p&lt;0.001) were significantly higher in patients with breast cancer than those without. The total cost of hospital stay was 26099 USD ($21814-$30383, p&lt;0.001) higher and LOS was 2.51 days higher (2.22-2.81; P&lt;0.001) when patients developed acute DVT however there was no significant difference in these parameters with PE hospitalizations. Conclusions: Our study concludes that patients with breast cancer who develop acute DVT or PE tend to have worse outcomes in terms of mortality. Length of hospital stay and total hospital charge are also increased in patients developing DVT. Current guidelines (2020) recommend against offering thromboprophylaxis to patients with any cancer. Given the worse outcomes, this study reflects the need to formulate risk assessment models for role of primary thromboprophylaxis in breast cancer.
Association of HER2/CEP17 ratio with pCR after HER2-directed neoadjuvant treatments in the phase III NeoALTTO trial.
Authors: Christian F. Singer, Franz König, Stephanie Kacerovsky-Strobl, Sabine Danzinger, Christine Brunner, Christoph Suppan, Christine Deutschmann, Marija Balic, Richard Greil, Daniel Egle, Evandro de Azambuja, Serena Di Cosimo, Michael Gnant
Journal: Journal of Clinical Oncology (2023)

600 Background: HER2-directed therapies are approved for the treatment of patients with HER2-positive invasive breast cancer as defined by HER2 protein overexpression, or HER2 gene amplification based on HER2/chromosome enumeration probe (CEP17) ratio ≥ 2.2. Above this accepted HER2 status determination, however, it is still unknown whether the efficacy of HER2-directed therapy in early breast cancer increases with increasing HER2/CEP17 ratios. Therefore, the purpose of the presented work is to evaluate whether quantitative assessment of the HER2/CEP17 ratio predicts pathological complete response (pCR) and event-free survival (EFS) of patients treated with neo-adjuvant HER2-based regimen in the prospective phase III NeoALTTO trial. Methods: 455 women with HER2-positive early breast cancer, who had received neo-adjuvant trastuzumab and/or lapatinib for 6 weeks and then together with 12 cycles of weekly paclitaxel were included in this analysis. The HER2/CEP17 ratio in the primary tumor samples was correlated with pCR and survival outcome. Results: The Median HER2/CEP17 ratio in NeoALTTO was 5.1 (range: 1.1 – 100.0), and ratios were not associated with age, hormone receptor status, or any of the other clinicopathological variables analyzed. The log HER2/CEP17 ratio significantly predicted for pCR in both uni-variate (OR: 1.83; 95% CI: 1.11 - 3.01, p = 0.0176) and multivariate analysis (OR: 1.79; 95% CI: 1.07 - 2.99, p = 0.0257). Higher HER2/CEP17 ratios were, however, not associated with improved EFS (adjusted HR = 0.79; p = 0.3537). A pCR prediction model which included HER2/CEP17 ratio, treatment arm, and hormone receptor status improved the predictive strength of treatment arm alone from a ROC AUC value of 0.60 to 0.69. Conclusions: In patients treated with HER2-based neoadjuvant therapy, quantitative analysis of the readily available pre-treatment HER2/CEP17 ratio by FISH is predictive of pCR but not EFS.
Utilizing Drug Combo to improve the design of phase 1 trials for anticancer drugs.
Authors: Lei Wang, Shijun Zhang, Lai Wei, Dwight Hall Owen, Lang Li
Journal: Journal of Clinical Oncology (2023)

1571 Background: Accurate prior knowledge of a single drug or drug combination on toxicity, pharmacokinetics (PK), maximum tolerated dose (MTD), and dose-limiting toxicity (DLT) can help oncologists and statisticians design an efficient phase 1 clinical trial. However, most of the relevant knowledge is shattered in various databases. More importantly, MTD and DLT are missing in any databases. This study constructed a comprehensive knowledge base with expert-curated MTD and DLT knowledge from published anticancer drug combination phase 1 clinical trial results. We also demonstrated the utility of the knowledgebase to design a more efficient phase 1 trial. Methods: Toxicity, pharmacology, and PK knowledge were integrated from publicly available data sources, including DrugBank, SIDER, and drug labels. An interdisciplinary team manually curated the MTD and DLT knowledge. Relevant papers were discovered by PubMed keyword search (drug name + MTD or RP2D). Details regarding patient eligibility, treatment information (dose, formula, and schedule), trial design method, dose levels, DLT definition, MTD or Recommended phase 2 dose, and DLT incident were curated in the structured data format and stored in a database. Rxnorm and MedDRA were used for normalizing drug names and DLT. Results: Currently, 2358 papers on cancer drugs phase 1 trial have been curated. Additionally, pharmacological and PK knowledge of 197 cancer drugs, ADE information from 1094 drug labels, and more than 500000 pieces of DDI evidence were integrated into the knowledge base. This knowledge base (drugcombo.info) could help researchers design an efficient phase 1 trial. For example, based on knowledge from Drug Combo, the combination of nivolumab and axitinib did not have potential PK DDI and severe overlapping toxicity. The reviewing physician considered a phase 1b study to start at a curated 5 mg maximum dose of axitinib with dose reduction to 3 and 2 mg and maintenance of a 3-mg/kg dose of nivolumab based on guidelines. With such additional preliminary knowledge, the reviewing statistician felt the adaptive statistical methods could be applied to redesign this phase I combination trial and minimize patient exposure to sub-therapeutic dose levels. Conclusions: To our knowledge, Drug Combo is the first knowledge that integrated curated MTD and DLT knowledge. It would promote applying curated knowledge to better design phase I trials to treat cancer.
Electronic health record (EHR) and genomics-based machine learning (ML) to predict therapeutic effectiveness among patients with hormone-receptor positive (HR)+/HER2- advanced breast cancer (aBC).
Authors: Deep K. Hathi, Steven Pisano, Hossein Honarvar, Ravi Bharat Parikh, Rahul K. Das
Journal: Journal of Clinical Oncology (2023)

e13574 Background: Patients (pts) with HR+/HER2- aBC may eventually become resistant to endocrine therapies and CDK4/6 inhibitors (CDK4/6i). ML algorithms on EHR linked with NGS data may enable more accurate predictions of therapeutic resistance and identify clinicogenomic (CG) risk factors. Methods: HR+/HER2- aBC pts treated between Jan 2015 – Dec 2022 with at least one NGS test report before line end were identified in ConcertAI’s Genome360 oncology database, consisting of molecular alterations abstracted from NGS reports linked with deeply curated US-based EHR. XGBoost (XGB) and Cox proportional hazards (CoxPH) models were trained to predict real-world progression-free survival (rwPFS) derived from curated tumor progression (TP), as assessed by the providers based on clinical observations, pathology results, and radiologic evidence. Data was split into 60:20:20 for training/validation/test. Pts in the 1st and 4th quartiles of predicted risk from XGB model were defined as low- and high-risk. Predictors were identified from Shapley Additive exPlanations (SHAP)-derived marginal hazard ratios (HR), derived in the 180 days prior to index date, and included demographics, labs, vitals, comorbidities, concomitant medications, procedures, tumor properties, and molecular alterations as features. Results: Among 624 pts (CDK4/6i treated = 519) with median age = 62.5 years and 10.5% black, 46.8% developed TP. The test cumulative dynamic ROC-AUC in the best-performing XGB model was 0.68. The precision at 180 days (%TP = 27) was 40%. The 180-day cumulative incidence of TP in low- and high-risk groups was 11.6% vs 37.7%. Higher line number, lower hemoglobin (HGB), higher alkaline phosphatase (ALP), presence of liver metastasis, and history of thoracic radiation were clinical risk factors. Genomic risk factors were FGF aberrations, alterations in TP53 and in genes within the MAPK, cell cycle, and ESR pathways, and co-alterations in the PIK3CA+TP53, and MAPK+ESR pathways. ALP-by-TP53 interaction was found. ALP level was a risk factor in TP53-wildtype (wt) but not in TP53-mutant (mut) pts. Conclusions: ML on EHR linked with NGS data enables identification of high-risk pts and multimodal predictors of CDK4/6i resistance among women with HR+/HER2- aBC, identifying both known and undescribed interactions between clinical and genomic risk factors as well as co-alteration risk factors. [Table: see text]